Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Journal article
Title: A soft fault-concept diagnosis method of analog circuits based on cloud model theory
Author: Liu, Q.
Cui, J.
Zhao, H.
Shen, H.
Citation: Journal of Networks, 2013; 8(7):1497-1503
Publisher: Academy Publisher
Issue Date: 2013
ISSN: 1796-2056
Statement of
Qi Liu, Jun Cui, HongDong Zhao and Hong Shen
Abstract: As the component parameters of analog circuits are influenced by tolerance, a soft fault-concept diagnosis model in this paper, which is based on the qualitative concepts of cloud model theory, is proposed to achieve the complete description of fault states and the fast diagnosis. The diagnosis model uses the cloud transform method to represent the conceptual soft classification of the multiple test-point voltage intervals. At first the model generates the atomic concepts of test-point voltages which are indicated by the characteristic values of cloud model, and promotes these atomic concepts to the qualitative concepts of test-point voltage which are easy to be understood. Then the model forms the fault category concept table based on a combination of qualitative concepts of multiple test-point voltages which are associated with the range of fault component parameters. Lastly according to the table, the soft fault-concept classification and diagnosis of analog circuits are achieved in this paper based on these formalization concepts. The simulation results show that the soft fault-concept method provides a good solution of analog circuits fault diagnosis avoiding the effect of component tolerance, at the same time the method has achieved a higher diagnosis accuracy rate.
DOI: 10.4304/jnw.8.7.1497-1503
Published version:
Appears in Collections:Aurora harvest 8
Computer Science publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.